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Netlab Reference Manual rbfgrad
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<H1> rbfgrad
</H1>
<h2>
Purpose
</h2>
Evaluate gradient of error function for RBF network.

<p><h2>
Synopsis
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<PRE>

g = rbfgrad(net, x, t)
[g, gdata, gprior] = rbfgrad(net, x, t)
</PRE>


<p><h2>
Description
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<CODE>g = rbfgrad(net, x, t)</CODE> takes a network data structure <CODE>net</CODE>
together with a matrix <CODE>x</CODE> of input
vectors and a matrix <CODE>t</CODE> of target vectors, and evaluates the gradient
<CODE>g</CODE> of the error function with respect to the network weights (i.e.
including the hidden unit parameters). The error
function is sum of squares.
Each row of <CODE>x</CODE> corresponds to one
input vector and each row of <CODE>t</CODE> contains the corresponding target vector.
If the output function is <CODE>'neuroscale'</CODE> then the gradient is only
computed for the output layer weights and biases.

<p><CODE>[g, gdata, gprior] = rbfgrad(net, x, t)</CODE> also returns separately 
the data and prior contributions to the gradient. In the case of
multiple groups in the prior, <CODE>gprior</CODE> is a matrix with a row
for each group and a column for each weight parameter.

<p><h2>
See Also
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<CODE><a href="rbf.htm">rbf</a></CODE>, <CODE><a href="rbffwd.htm">rbffwd</a></CODE>, <CODE><a href="rbferr.htm">rbferr</a></CODE>, <CODE><a href="rbfpak.htm">rbfpak</a></CODE>, <CODE><a href="rbfunpak.htm">rbfunpak</a></CODE>, <CODE><a href="rbfbkp.htm">rbfbkp</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
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<p>Copyright (c) Ian T Nabney (1996-9)


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